# -*- coding: utf-8 -*-

from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta
from jms.dm.dm_site_digital_real_detail_dt import jms_dm__dm_site_digital_real_detail_dt

jms_dm__dm_site_digital_real_sum_dt = SparkSqlOperator(
    task_id='jms_dm__dm_site_digital_real_sum_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    name='jms_dm__dm_site_digital_real_sum_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_site_digital_real_sum_dt/execute.hql',
    driver_memory='4G',
    executor_cores=4,
    executor_memory='8G',
    num_executors=5,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled'                  : 'true',  # 动态资源开启
          'spark.shuffle.service.enabled'                    : 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors'             : 10,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode'         : 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead'                    : '1G',  # 堆外内存
          'spark.sql.shuffle.partitions'                     : 400,
          },
    #hiveconf={'hive.exec.dynamic.partition'             : 'true',  # 动态分区
#              'hive.exec.dynamic.partition.mode'        : 'nonstrict',
#              'hive.exec.max.dynamic.partitions'        : 20,  # 每天生成 20 个分区
#              'hive.exec.max.dynamic.partitions.pernode': 20,  # 每天生成 20 个分区
#              },
    execution_timeout=timedelta(hours=1),
    email=['leichao@yl-scm.com','yl_bigdata@yl-scm.com'],
)

jms_dm__dm_site_digital_real_sum_dt << [
    jms_dm__dm_site_digital_real_detail_dt
]
